Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

Simple Summary Diffuse gliomas (DGs) are classified according to several histomolecular criteria, including the gliomagenesis pathway, based on IDH mutational status. The aim of our retrospective study was to identify the metabolomic signatures of the gliomagenesis pathway and grade of DG by using an untargeted metabolomic technique and to evaluate their diagnostic performances on tumor samples that are formalin-fixed and paraffin-embedded (FFPE). We identified three metabolites, including two novel metabolites of interest in DG that enable prediction of IDH mutational status or grade in FFPE samples. We showed that untargeted metabolomics technique may be performed on FFPE samples and be a useful tool for research studies on large cohorts.Abstract The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.